HHS Public Access Author manuscript Author Manuscript
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01. Published in final edited form as: Psychiatr Serv. 2016 April 1; 67(4): 397–404. doi:10.1176/appi.ps.201500116.
The Concentration of Opioid Prescriptions by Providers and Patients in the Oregon Medicaid Program Hyunjee Kim, Oregon Health and Science University - Center for Health Systems Effectiveness Portland, Oregon
Author Manuscript
Daniel Hartung, Oregon State University Lorie Jacob, Oregon Health and Science University - Center for Health Systems Effectiveness Portland, Oregon Dennis McCarty, and Oregon Health & Science University - Public Health and Preventive Medicine K. John McConnell Oregon Health & Science University - Emergency Medicine Hyunjee Kim: [email protected]
Author Manuscript
Abstract Objective—This study seeks to understand the distribution of opioid prescribing across providers and patients, and examines how this concentration predicts opioid misuse. Methods—Using 2013 Oregon Medicaid claims and National Provider ID registry, this study identified patients who filled at least one opioid prescription and providers who prescribed opioids for those patients (N=61,477 Medicaid patients). This study examined the distribution of opioid prescriptions by provider and patient, the extent to which high-volume opioid use was associated with potential opioid misuse, and how this association changes when patients received opioids from providers in the top decile of morphine equivalents (MEQ) prescribed in 2013. This study used four indicators of opioid misuse: doctor and pharmacy shopping for opioid prescriptions, opioid prescription overlap, and opioid and benzodiazepine prescription overlap.
Author Manuscript
Results—Opioid use and prescriptions were heavily concentrated among the top 10% opioid users and prescribers. Those high-volume opioid users and prescribers accounted for 83.2% and 80.8% in MEQ of entire opioids prescribed. Patients’ increasing use of MEQ was associated with most measures of opioid misuse. Patients receiving opioids from high-volume prescribers had a higher probability of opioid prescription overlap and opioid and benzodiazepine prescription
Other authors have no conflicts of interest. This study has been presented at the research seminar at the Center to Improve Veteran Involvement in Care, Portland Oregon (on March 6, 2015)
Kim et al.
Page 2
Author Manuscript
overlap, but the increase was significant only among patients who received high doses of opioids and the size of the increase was modest. Conclusions—Whereas current policies emphasize reducing opioid prescriptions across all patients and providers, study results suggest potential for policies that focus on high-volume opioid users and prescribers.
Author Manuscript
Death rates from prescription opioid overdoses in the United States more than tripled between 1999 and 2012, creating an urgent need for policies that can effectively reduce the mortality and morbidity associated with opioid misuse.1 The effectiveness of policies may depend, in part, on how providers prescribe opioids. If, for example, providers are uniform in the ways in which they prescribe opioids, then policies that broadly apply to all providers may be warranted. On the other hand, if the majority of opioids are obtained through a relatively small number of providers or a certain provider specialty, it may be more effective to tailor policy interventions to affect a narrower group of providers. Opioid misuse is generally concentrated among patients who are prescribed higher volumes of opioids.2–5 However, less is known about variations in provider prescribing patterns and their implications. Heavy prescribing could be a marker for inappropriate use. In a recent study, opioid-related deaths were concentrated among patients treated by physicians who prescribed high volumes of opioids for each patient, although this study did not control for patient characteristics that could potentially affect patient health outcomes.6 On the other hand, high-volume providers could be associated with lower rates of opioid misuse and better patient outcomes because they specialize in pain treatment.
Author Manuscript
This study begins with simple questions for which we have relatively little data: within a specified region and patient population, how concentrated is opioid prescribing and use and does this concentration predict opioid misuse? Specifically, we seek to understand whether the top decile of providers and patients account for a majority of opioid prescription and test the relationships of the opioid prescription concentration to indicators of opioid misuse.
Author Manuscript
We focused on Medicaid enrollees in Oregon. Compared to privately-insured individuals, Medicaid patients are more likely to receive opioid prescriptions, at higher doses of opioids, for longer periods of time,7–9 and have higher death rates from overdoses.10,11 Oregon has taken a progressive stance in providing access to opioids for treatment of pain. The Oregon Intractable Pain Act (1995) allows physicians to prescribe opioids for treatment of chronic pain at levels which could trigger sanctions from state medical boards elsewhere in the country.12 This law has been celebrated by advocates concerned about inadequate pain treatment. Nonetheless, Oregon’s death and hospitalization rates from prescription opioid overdose increased more than five-fold between 2000 and 2011,13 leading to calls for policies to limit opioid prescribing. Although there has been widespread speculation about the ways in which opioids are obtained and subsequently abused, relatively little is known about what heavy users look like, and even less is known about different provider prescribing patterns. The goal of this study is to assess these patterns to provide insight for policies aimed at reducing opioid misuse among a vulnerable population of Medicaid enrollees.
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 3
Author Manuscript
METHODS Data We used year 2013 data from Oregon Medicaid claims for opioid users’ demographics, health characteristics, and opioid use patterns and National Provider Registry for prescribers’ basic characteristics. We combined these two datasets using each provider’s national provider ID, creating a patient-provider linked data set. We limited the sample to Medicaid beneficiaries who filled opioid prescriptions at least once in year 2013 and providers who prescribed opioid for those beneficiaries. We further excluded patients who were diagnosed with cancer at any time in 2013, were not continuously enrolled in Medicaid, and were eligible for Medicare. The final dataset includes 61,477 Medicaid beneficiaries.
Author Manuscript
Potential Opioid Misuse Measures We used four measures of potential opioid misuse capturing inappropriate opioid prescribing practices and patient behaviors. Each measure indicates whether the respective practice or behavior occurred at least once in 2013. Two measures reflect illicit patient behaviors: doctor and pharmacy shopping for opioid prescriptions. We defined doctor shopping as having received opioid prescriptions from six or more providers during one year and pharmacy shopping as getting opioid prescriptions filled from eight or more pharmacies.14 Previous studies found that patients involved with doctor/pharmacy shopping were more likely to be associated with opioid overdose death.14,15
Author Manuscript
We also used two indicators of inappropriate opioid prescribing practices: opioid prescription overlap (i.e., at least one-week overlap for two prescriptions of same opioid drugs from either single or multiple prescribers) and opioid and benzodiazepine prescription overlap (at least one-week overlap). All the measures of prescribing practices have been used in previous studies.16–19 Concentration of Opioid Prescriptions
Author Manuscript
We defined high-volume opioid users and prescribers as those in the top decile in morphine equivalents (MEQ) prescribed throughout the year.8 In this definition, we used MEQ to make opioid doses comparable across products. We preferred MEQ as a measure of concentration to the number of prescriptions because opioid doses differ across prescriptions. To calculate MEQ for each prescription, the quantity of pills/patches dispensed was multiplied by the strength of each pill/patch. We then multiplied this by a morphine equivalents conversion factor (Appendix Table 2). Lastly, we summed MEQ of all prescriptions written throughout 2013 for each patient and prescriber. Prescribers’ high annual MEQ could be attributable to multiple factors. For example, highvolume prescribers could just prescribe a moderate amount of opioids for many patients. Alternatively, they might prescribe high doses of opioids for a moderate number of patients. To distinguish these two groups, in our sensitivity analysis, we used an alternative definition of high-volume opioid prescribers: those in the top decile in annual MEQ prescribed per patient.
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 4
Other Variables
Author Manuscript
We controlled for patient age, gender, rurality20, and pain-related diagnoses.21,22 We also controlled for health conditions which often co-occur among opioid users, including depression, bipolar, schizophrenia, alcohol problems, and drug problems (Appendix Table 1).23–26 For risk adjustment, we used the Chronic Illness and Disability Payment System indicators, which have been validated and used for risk adjustment in Medicaid populations.27 We also accounted for each provider’s gender, entity type (sole or group practice) and provider type/specialty. Lastly, we controlled for each patient’s health plan.12 Data Analysis
Author Manuscript
We conducted two sets of regression analyses. First, to examine to what extent high-volume opioid users are involved with opioid misuse, we estimated a patient-level regression of four measures of opioid misuse controlling for a patient’s MEQ decile group, demographics, comorbidities, and Medicaid health plan. The second set of regressions controlled for prescriber characteristics including gender, entity type, and provider type/specialty. We also added an interaction term between a patient’s MEQ decile group and a dummy variable indicating whether her provider was a high-volume prescriber in the top MEQ decile. We considered this interaction term because opioid users’ experience of opioid misuse could be influenced by whether they were prescribed by a high-volume prescriber. In the second set of regressions, we did not examine doctor/pharmacy shopping as outcome variables because the number of doctors/pharmacies each patient visits for opioid prescription would be unlikely to be affected by her prescriber.
Author Manuscript
In the second set of regressions, we assigned one prescriber to each patient to take into account provider characteristics in the patient-level regression analyses. Many patients (44%) had, however, more than one prescriber. For patients with multiple prescribers, we selected the provider who prescribed opioids the most times and accounted for at least one third of the patient’s total number of prescriptions. Based on this assignment rule, 11,712 out of 61,477 patients were dropped from the original sample because they did not have a provider who meets the listed condition. As a sensitivity analysis, we examined different assignment rules. All regressions used a linear-probability model instead of logit regressions to avoid a sample size reduction caused by perfect prediction. Standard-errors were clustered at the providerlevel.
Author Manuscript
RESULTS A relatively small share of providers account for the majority of all opioids prescribed. The top 10% of providers accounted for 80.8% of all MEQ provided to Oregon Medicaid beneficiaries. The top 1% of providers accounted for 43.4% of MEQ prescribed. As seen in Figure 1(a), the majority of providers (those under the top 10% reference line on the y-axis) prescribed relatively low amounts of MEQ. The average annual MEQ prescribed by providers in deciles 1–9 was 7,267; providers in the top decile prescribed an average annual MEQ of 275,503. High-volume opioid prescribers in the top decile also had prescription
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 5
Author Manuscript
counts that were more than five times higher than the average counts of prescribers in deciles 1–9. We found a similar concentration of opioid use among patients. The top 10% of patients accounted for 83.2% of all MEQ prescribed to Oregon Medicaid beneficiaries. The top 1% of patients accounted for 27.7% of MEQ prescribed. As seen in Figure 1(b), the majority of patients (those under the top 10% reference line on the y-axis) received relatively low volumes of MEQ. The average annual MEQ prescribed to patients in deciles 1–9 was 863; patients in the top decile averaged annual MEQ of 38,368 and received eight times more prescriptions than those in deciles 1–9.
Author Manuscript
Table 1 displays differences across Medicaid beneficiaries who received no opioid prescriptions, opioid users in lower MEQ decile 1–9, and those in the top MEQ decile. Almost one in five (18%) Medicaid beneficiaries received prescriptions for opioids. Compared to individuals who received no opioid prescriptions, individuals with an opioid prescription were older, more likely to be female, and had higher rates of joint and back pain diagnoses as well as diagnoses for depression. The mean daily dose for individuals in the top decile was 87.6mg MEQ, close to the 100mg level where overdose risks dramatically increase.2,19 High-volume users in the top decile were far more likely to experience doctor/ pharmacy shopping, overlap in opioid prescriptions, and opioid and benzodiazepine overlap.
Author Manuscript
Table 2 displays differences across providers who never prescribed opioids, those in the lower nine deciles, and providers in the top decile of prescribers. Two of three prescribers (66%) who wrote prescriptions for Medicaid patients prescribed opioids. Provider type/ specialty varied substantially across all three groups. Lower-volume prescribers in MEQ decile 1–9 were more likely to be a physician in family medicine, emergency medicine, obstetrics/gynecology, physician assistant, or dentist. Among high-volume prescribers in the top decile, however, the proportion of physicians in emergency medicine and obstetrics/ gynecology and dentists was significantly lower (=9,905 mg MEQ
d
The standard deviation for # opioid prescription is 4.0 and 10.5 for patients in lower- and top MEQ decile, respectively.
e
The standard deviation for daily dose is 22.2 and 72.6 for patients in lower- and top MEQ decile, respectively.
Author Manuscript Author Manuscript Author Manuscript Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 15
Table 2
Author Manuscript
Basic characteristics and opioid prescription pattern across provider groups defined by opioid prescription Opioid prescribers Opioid nonprescribers (N=5,153)
Providers in lower MEQ decilesa (N=8,923)
Providers in top MEQ decileb (N=991)
52.7
56.7
56.6
Sole practice
19.7
16.5
14.4
Group practice
80.3
83.5
85.6
MD: Family medicine
7.1
13.0
39.5
MD: Internal medicine
20.6
13.7
23.4
2.9
9.0
.9
Male % Organization %
Type of providers %
Author Manuscript
MD: Emergency medicine MD: Ob/Gyn
Author Manuscript
2.0
5.5
.3
Nurse practitioner
11.6
8.2
14.4
Physician assistant
4.5
8.7
9.5
Dentist
4.6
10.6
.5
Othersc
46.7
31.4
11.5
# Medicaid patients they prescribed opioidd
--
17.4
42.5
Daily dose (MEQ mg) in prescriptione
--
46.3
77.7
Percent of providers ever prescribing >100mg MEQ per day %
--
34.4
96.8
Percent of providers ever prescribing >200mg MEQ per day %
--
8.2
68.1
Opioid prescription pattern
a
Lower deciles indicate providers in deciles 1 through 9 in MEQ.
b
Providers in top decile got prescribed >=68,383 mg MEQ
c
Other types of providers include 69 different types of providers including MDs in orthopedic surgery, neurology, pediatrics, and etc.
d
The standard deviation for # Medicaid patients they prescribed opioid is 31.5 and 47.0 for opioid prescribers in lower and top decile, respectively.
e The standard deviation for daily dose in prescription is 31.1 and 49.9 for opioid prescribers in lower and top decile, respectively.
Author Manuscript Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Author Manuscript .0011 .0011 .0013 .0025 .0055 .0055 .0048
−.0040*** −.0094*** −.0128*** .0051** .1048*** .1099*** .0796***
Decile 5 (MEQ ≥151.2)
Decile 6 (MEQ ≥240)
Decile 7 (MEQ ≥390)
Decile 8 (MEQ ≥750)
Decile 9 (MEQ ≥2,228)
Decile 10 (MEQ ≥9,905)
.0065***
.0049***
.0021***
−.0011***
−.0007***
−.0005***
−.0002
.0000
.0000
--
Coef
.0011
.0010
.0008
.0002
.0002
.0001
.0001
.0001
.0001
--
SE
.2488***
.1338***
.0216***
.0008
−.0007
−.0002
−.0003
.0005
−.0003
--
Coef
.0069
.0053
.0026
.0012
.0008
.0006
.0005
.0005
.0005
--
SE
.2988***
.1613***
.0673***
.0199***
.0088***
.0014
.0033**
−.0018
−.0027***
--
Coef
.0067
.0054
.0040
.0024
.0018
.0013
.0014
.0010
.0010
--
SE
(4) Opioid/benzodiazepine overlap (N=61,477)
c *** p
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01. Published in final edited form as: Psychiatr Serv. 2016 April 1; 67(4): 397–404. doi:10.1176/appi.ps.201500116.
The Concentration of Opioid Prescriptions by Providers and Patients in the Oregon Medicaid Program Hyunjee Kim, Oregon Health and Science University - Center for Health Systems Effectiveness Portland, Oregon
Author Manuscript
Daniel Hartung, Oregon State University Lorie Jacob, Oregon Health and Science University - Center for Health Systems Effectiveness Portland, Oregon Dennis McCarty, and Oregon Health & Science University - Public Health and Preventive Medicine K. John McConnell Oregon Health & Science University - Emergency Medicine Hyunjee Kim: [email protected]
Author Manuscript
Abstract Objective—This study seeks to understand the distribution of opioid prescribing across providers and patients, and examines how this concentration predicts opioid misuse. Methods—Using 2013 Oregon Medicaid claims and National Provider ID registry, this study identified patients who filled at least one opioid prescription and providers who prescribed opioids for those patients (N=61,477 Medicaid patients). This study examined the distribution of opioid prescriptions by provider and patient, the extent to which high-volume opioid use was associated with potential opioid misuse, and how this association changes when patients received opioids from providers in the top decile of morphine equivalents (MEQ) prescribed in 2013. This study used four indicators of opioid misuse: doctor and pharmacy shopping for opioid prescriptions, opioid prescription overlap, and opioid and benzodiazepine prescription overlap.
Author Manuscript
Results—Opioid use and prescriptions were heavily concentrated among the top 10% opioid users and prescribers. Those high-volume opioid users and prescribers accounted for 83.2% and 80.8% in MEQ of entire opioids prescribed. Patients’ increasing use of MEQ was associated with most measures of opioid misuse. Patients receiving opioids from high-volume prescribers had a higher probability of opioid prescription overlap and opioid and benzodiazepine prescription
Other authors have no conflicts of interest. This study has been presented at the research seminar at the Center to Improve Veteran Involvement in Care, Portland Oregon (on March 6, 2015)
Kim et al.
Page 2
Author Manuscript
overlap, but the increase was significant only among patients who received high doses of opioids and the size of the increase was modest. Conclusions—Whereas current policies emphasize reducing opioid prescriptions across all patients and providers, study results suggest potential for policies that focus on high-volume opioid users and prescribers.
Author Manuscript
Death rates from prescription opioid overdoses in the United States more than tripled between 1999 and 2012, creating an urgent need for policies that can effectively reduce the mortality and morbidity associated with opioid misuse.1 The effectiveness of policies may depend, in part, on how providers prescribe opioids. If, for example, providers are uniform in the ways in which they prescribe opioids, then policies that broadly apply to all providers may be warranted. On the other hand, if the majority of opioids are obtained through a relatively small number of providers or a certain provider specialty, it may be more effective to tailor policy interventions to affect a narrower group of providers. Opioid misuse is generally concentrated among patients who are prescribed higher volumes of opioids.2–5 However, less is known about variations in provider prescribing patterns and their implications. Heavy prescribing could be a marker for inappropriate use. In a recent study, opioid-related deaths were concentrated among patients treated by physicians who prescribed high volumes of opioids for each patient, although this study did not control for patient characteristics that could potentially affect patient health outcomes.6 On the other hand, high-volume providers could be associated with lower rates of opioid misuse and better patient outcomes because they specialize in pain treatment.
Author Manuscript
This study begins with simple questions for which we have relatively little data: within a specified region and patient population, how concentrated is opioid prescribing and use and does this concentration predict opioid misuse? Specifically, we seek to understand whether the top decile of providers and patients account for a majority of opioid prescription and test the relationships of the opioid prescription concentration to indicators of opioid misuse.
Author Manuscript
We focused on Medicaid enrollees in Oregon. Compared to privately-insured individuals, Medicaid patients are more likely to receive opioid prescriptions, at higher doses of opioids, for longer periods of time,7–9 and have higher death rates from overdoses.10,11 Oregon has taken a progressive stance in providing access to opioids for treatment of pain. The Oregon Intractable Pain Act (1995) allows physicians to prescribe opioids for treatment of chronic pain at levels which could trigger sanctions from state medical boards elsewhere in the country.12 This law has been celebrated by advocates concerned about inadequate pain treatment. Nonetheless, Oregon’s death and hospitalization rates from prescription opioid overdose increased more than five-fold between 2000 and 2011,13 leading to calls for policies to limit opioid prescribing. Although there has been widespread speculation about the ways in which opioids are obtained and subsequently abused, relatively little is known about what heavy users look like, and even less is known about different provider prescribing patterns. The goal of this study is to assess these patterns to provide insight for policies aimed at reducing opioid misuse among a vulnerable population of Medicaid enrollees.
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 3
Author Manuscript
METHODS Data We used year 2013 data from Oregon Medicaid claims for opioid users’ demographics, health characteristics, and opioid use patterns and National Provider Registry for prescribers’ basic characteristics. We combined these two datasets using each provider’s national provider ID, creating a patient-provider linked data set. We limited the sample to Medicaid beneficiaries who filled opioid prescriptions at least once in year 2013 and providers who prescribed opioid for those beneficiaries. We further excluded patients who were diagnosed with cancer at any time in 2013, were not continuously enrolled in Medicaid, and were eligible for Medicare. The final dataset includes 61,477 Medicaid beneficiaries.
Author Manuscript
Potential Opioid Misuse Measures We used four measures of potential opioid misuse capturing inappropriate opioid prescribing practices and patient behaviors. Each measure indicates whether the respective practice or behavior occurred at least once in 2013. Two measures reflect illicit patient behaviors: doctor and pharmacy shopping for opioid prescriptions. We defined doctor shopping as having received opioid prescriptions from six or more providers during one year and pharmacy shopping as getting opioid prescriptions filled from eight or more pharmacies.14 Previous studies found that patients involved with doctor/pharmacy shopping were more likely to be associated with opioid overdose death.14,15
Author Manuscript
We also used two indicators of inappropriate opioid prescribing practices: opioid prescription overlap (i.e., at least one-week overlap for two prescriptions of same opioid drugs from either single or multiple prescribers) and opioid and benzodiazepine prescription overlap (at least one-week overlap). All the measures of prescribing practices have been used in previous studies.16–19 Concentration of Opioid Prescriptions
Author Manuscript
We defined high-volume opioid users and prescribers as those in the top decile in morphine equivalents (MEQ) prescribed throughout the year.8 In this definition, we used MEQ to make opioid doses comparable across products. We preferred MEQ as a measure of concentration to the number of prescriptions because opioid doses differ across prescriptions. To calculate MEQ for each prescription, the quantity of pills/patches dispensed was multiplied by the strength of each pill/patch. We then multiplied this by a morphine equivalents conversion factor (Appendix Table 2). Lastly, we summed MEQ of all prescriptions written throughout 2013 for each patient and prescriber. Prescribers’ high annual MEQ could be attributable to multiple factors. For example, highvolume prescribers could just prescribe a moderate amount of opioids for many patients. Alternatively, they might prescribe high doses of opioids for a moderate number of patients. To distinguish these two groups, in our sensitivity analysis, we used an alternative definition of high-volume opioid prescribers: those in the top decile in annual MEQ prescribed per patient.
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 4
Other Variables
Author Manuscript
We controlled for patient age, gender, rurality20, and pain-related diagnoses.21,22 We also controlled for health conditions which often co-occur among opioid users, including depression, bipolar, schizophrenia, alcohol problems, and drug problems (Appendix Table 1).23–26 For risk adjustment, we used the Chronic Illness and Disability Payment System indicators, which have been validated and used for risk adjustment in Medicaid populations.27 We also accounted for each provider’s gender, entity type (sole or group practice) and provider type/specialty. Lastly, we controlled for each patient’s health plan.12 Data Analysis
Author Manuscript
We conducted two sets of regression analyses. First, to examine to what extent high-volume opioid users are involved with opioid misuse, we estimated a patient-level regression of four measures of opioid misuse controlling for a patient’s MEQ decile group, demographics, comorbidities, and Medicaid health plan. The second set of regressions controlled for prescriber characteristics including gender, entity type, and provider type/specialty. We also added an interaction term between a patient’s MEQ decile group and a dummy variable indicating whether her provider was a high-volume prescriber in the top MEQ decile. We considered this interaction term because opioid users’ experience of opioid misuse could be influenced by whether they were prescribed by a high-volume prescriber. In the second set of regressions, we did not examine doctor/pharmacy shopping as outcome variables because the number of doctors/pharmacies each patient visits for opioid prescription would be unlikely to be affected by her prescriber.
Author Manuscript
In the second set of regressions, we assigned one prescriber to each patient to take into account provider characteristics in the patient-level regression analyses. Many patients (44%) had, however, more than one prescriber. For patients with multiple prescribers, we selected the provider who prescribed opioids the most times and accounted for at least one third of the patient’s total number of prescriptions. Based on this assignment rule, 11,712 out of 61,477 patients were dropped from the original sample because they did not have a provider who meets the listed condition. As a sensitivity analysis, we examined different assignment rules. All regressions used a linear-probability model instead of logit regressions to avoid a sample size reduction caused by perfect prediction. Standard-errors were clustered at the providerlevel.
Author Manuscript
RESULTS A relatively small share of providers account for the majority of all opioids prescribed. The top 10% of providers accounted for 80.8% of all MEQ provided to Oregon Medicaid beneficiaries. The top 1% of providers accounted for 43.4% of MEQ prescribed. As seen in Figure 1(a), the majority of providers (those under the top 10% reference line on the y-axis) prescribed relatively low amounts of MEQ. The average annual MEQ prescribed by providers in deciles 1–9 was 7,267; providers in the top decile prescribed an average annual MEQ of 275,503. High-volume opioid prescribers in the top decile also had prescription
Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 5
Author Manuscript
counts that were more than five times higher than the average counts of prescribers in deciles 1–9. We found a similar concentration of opioid use among patients. The top 10% of patients accounted for 83.2% of all MEQ prescribed to Oregon Medicaid beneficiaries. The top 1% of patients accounted for 27.7% of MEQ prescribed. As seen in Figure 1(b), the majority of patients (those under the top 10% reference line on the y-axis) received relatively low volumes of MEQ. The average annual MEQ prescribed to patients in deciles 1–9 was 863; patients in the top decile averaged annual MEQ of 38,368 and received eight times more prescriptions than those in deciles 1–9.
Author Manuscript
Table 1 displays differences across Medicaid beneficiaries who received no opioid prescriptions, opioid users in lower MEQ decile 1–9, and those in the top MEQ decile. Almost one in five (18%) Medicaid beneficiaries received prescriptions for opioids. Compared to individuals who received no opioid prescriptions, individuals with an opioid prescription were older, more likely to be female, and had higher rates of joint and back pain diagnoses as well as diagnoses for depression. The mean daily dose for individuals in the top decile was 87.6mg MEQ, close to the 100mg level where overdose risks dramatically increase.2,19 High-volume users in the top decile were far more likely to experience doctor/ pharmacy shopping, overlap in opioid prescriptions, and opioid and benzodiazepine overlap.
Author Manuscript
Table 2 displays differences across providers who never prescribed opioids, those in the lower nine deciles, and providers in the top decile of prescribers. Two of three prescribers (66%) who wrote prescriptions for Medicaid patients prescribed opioids. Provider type/ specialty varied substantially across all three groups. Lower-volume prescribers in MEQ decile 1–9 were more likely to be a physician in family medicine, emergency medicine, obstetrics/gynecology, physician assistant, or dentist. Among high-volume prescribers in the top decile, however, the proportion of physicians in emergency medicine and obstetrics/ gynecology and dentists was significantly lower (=9,905 mg MEQ
d
The standard deviation for # opioid prescription is 4.0 and 10.5 for patients in lower- and top MEQ decile, respectively.
e
The standard deviation for daily dose is 22.2 and 72.6 for patients in lower- and top MEQ decile, respectively.
Author Manuscript Author Manuscript Author Manuscript Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Kim et al.
Page 15
Table 2
Author Manuscript
Basic characteristics and opioid prescription pattern across provider groups defined by opioid prescription Opioid prescribers Opioid nonprescribers (N=5,153)
Providers in lower MEQ decilesa (N=8,923)
Providers in top MEQ decileb (N=991)
52.7
56.7
56.6
Sole practice
19.7
16.5
14.4
Group practice
80.3
83.5
85.6
MD: Family medicine
7.1
13.0
39.5
MD: Internal medicine
20.6
13.7
23.4
2.9
9.0
.9
Male % Organization %
Type of providers %
Author Manuscript
MD: Emergency medicine MD: Ob/Gyn
Author Manuscript
2.0
5.5
.3
Nurse practitioner
11.6
8.2
14.4
Physician assistant
4.5
8.7
9.5
Dentist
4.6
10.6
.5
Othersc
46.7
31.4
11.5
# Medicaid patients they prescribed opioidd
--
17.4
42.5
Daily dose (MEQ mg) in prescriptione
--
46.3
77.7
Percent of providers ever prescribing >100mg MEQ per day %
--
34.4
96.8
Percent of providers ever prescribing >200mg MEQ per day %
--
8.2
68.1
Opioid prescription pattern
a
Lower deciles indicate providers in deciles 1 through 9 in MEQ.
b
Providers in top decile got prescribed >=68,383 mg MEQ
c
Other types of providers include 69 different types of providers including MDs in orthopedic surgery, neurology, pediatrics, and etc.
d
The standard deviation for # Medicaid patients they prescribed opioid is 31.5 and 47.0 for opioid prescribers in lower and top decile, respectively.
e The standard deviation for daily dose in prescription is 31.1 and 49.9 for opioid prescribers in lower and top decile, respectively.
Author Manuscript Psychiatr Serv. Author manuscript; available in PMC 2017 April 01.
Author Manuscript .0011 .0011 .0013 .0025 .0055 .0055 .0048
−.0040*** −.0094*** −.0128*** .0051** .1048*** .1099*** .0796***
Decile 5 (MEQ ≥151.2)
Decile 6 (MEQ ≥240)
Decile 7 (MEQ ≥390)
Decile 8 (MEQ ≥750)
Decile 9 (MEQ ≥2,228)
Decile 10 (MEQ ≥9,905)
.0065***
.0049***
.0021***
−.0011***
−.0007***
−.0005***
−.0002
.0000
.0000
--
Coef
.0011
.0010
.0008
.0002
.0002
.0001
.0001
.0001
.0001
--
SE
.2488***
.1338***
.0216***
.0008
−.0007
−.0002
−.0003
.0005
−.0003
--
Coef
.0069
.0053
.0026
.0012
.0008
.0006
.0005
.0005
.0005
--
SE
.2988***
.1613***
.0673***
.0199***
.0088***
.0014
.0033**
−.0018
−.0027***
--
Coef
.0067
.0054
.0040
.0024
.0018
.0013
.0014
.0010
.0010
--
SE
(4) Opioid/benzodiazepine overlap (N=61,477)
c *** p
